Geospatial data based assessment of driver behavior

ABSTRACT

A method of geospatial data based assessment driver behavior to improve driver safety and efficiency is disclosed. A method of a server device may comprise determining that a telemetry data is associated with a vehicle communicatively coupled with the server device and comparing the telemetry data with a driver objective data. A variance between the telemetry data and the driver objective data may then be determined. A performance score may be generated upon comparison of the variance to a threshold limit and/or the driver objective data. The performance score may be published along with other performance scores of other drivers in other vehicles also communicatively coupled with the server device to a reporting dashboard module. Elements of game theory may be implemented to create a team driving challenge and/or a driver performance program to generate the performance score to improve driver safety and efficiency for commercial fleets.

CLAIM OF PRIORITY

This utility patent application is a Continuation-In-Part (CIP) of andincorporates by references in its entirety, U.S. Utility patentapplication Ser. No. 13/310,629 titled “ALERT GENERATION BASED ON AGEOGRAPHIC TRANSGRESSION OF A VEHICLE” and filed on Dec. 2, 2011, andU.S. Utility patent application Ser. No. 13/328,070 titled “GEOSPATIALDATA BASED MEASUREMENT OF RISK ASSOCIATED WITH A VEHICULAR SECURITYINTEREST IN A VEHICULAR LOAN PORTFOLIO” and filed on Dec. 16, 2011.

FIELD OF TECHNOLOGY

This disclosure relates generally to geospatial data based assessment ofdriver behavior with the goal of improving driver safety and efficiency,and in one example embodiment, using telemetry data associated with avehicle to determine a variance between the telemetry data and one ormore driver objectives and/or pattern of usage information and togenerate and publish a performance score associated with an individualdriver and/or a team and/or fleet of drivers. The performance score maybe utilized to incentivize and improve driver safety and efficiency ofthe individual driver and/or the team and/or fleet of drivers by usingcomponents of game theory.

BACKGROUND

Driver safety and efficiency is of paramount concern to any partyoperating a vehicle on roads and highways. Improving driver safety andefficiency is very important to a company running and/or managing afleet of commercial vehicles. Such commercial vehicle fleets aretypically comprised of trucks and other heavy duty vehicles that usuallytransport high value goods over vast distances. Other vehicle fleets mayalso use and/or operate passenger vehicles (e.g., taxi companies,security companies, etc.) to be operated off-highway. Therefore, partiesinterested in assessing one or more driver's safety and/or efficiencymay be interested in assessing the driving behavior of the driver of thevehicle in relation to the driving behavior of other drivers of othervehicles that are part of the same fleet. A non-punitive, yetchallenging competition between drivers may give individual drivers theincentive to drive safely and efficiently. Telemetry data from vehiclesmay give interested parties an understanding of the driver's drivingpatterns and may contribute to the assessment of safety and/orefficiency.

Interested parties may use and/or employ geospatial positioning devicesthat communicate geospatial data based on a worldwide navigational andsurveying facility dependent on the reception of signals from an arrayof orbiting satellites (e.g., Global Positioning System (GPS)technology). Another device might be a Real Time Locator System (RTLS)which uses Radio Frequency Identification (RFID) technology to transmitthe physical location of RFID tagged objects. In addition, suchgeospatial positioning devices may be placed directly within vehicles byOriginal Equipment Manufacturers (OEMs). For example, car manufacturersmay install OEM telematics solutions (e.g., OnStar™) within all theirvehicles.

The use of GPS, RTLS, RFID or OEM telematics based geospatialpositioning devices to enable the gathering of telemetry data is gainingprominence. Geospatial positioning devices are frequently used to trackand gather telemetry data associated with the vehicle. Certainlocations, driving behaviors and/or patterns of movement associated withthe driver and his/her vehicle may be indicative of an increased ordecreased safety and/or efficiency risk. Gathering such data indicativeof a driver's safety and/or efficiency may be useful to improve thesafety and/or efficiency of the driver and/or a fleet of drivers usingcomponents of game theory.

For example, one reliable indicator of the safety of a driver may be theacceleration rate of the driver's vehicle. If the vehicle accelerationis high, it is likely that the driver may be wasting gasoline andincreasing risks of accidents and other mishaps. This determination maybe extrapolated to analyze and assess the safety and/or efficiency riskof an entire fleet of vehicles and their corresponding individualdrivers. Therefore, what is needed is a method for utilizing geospatialdata (e.g., locational data associated with the a vehicle) to assessdriver behavior by gathering and using telemetry data associated withthe vehicle to improve driver safety and efficiency by incorporatingcomponents of game theory (e.g., mathematics, statistics, economics, andpsychology) to incentivize and motivate drivers to drive safely andefficiently.

SUMMARY

A method of geospatial data based assessment of driver behavior isdisclosed. In one aspect, the method may involve determining that atelemetry data is associated with a vehicle that is communicativelycoupled to a server device. The method may also involve comparing thetelemetry data with a driver objective data associated with the vehicle,determining a variance between the telemetry data and the driverobjective data, generating a performance score upon comparison of thevariance to the driver objective data and/or a threshold limit, andpublishing the performance score along with other performance scores ofother drivers in other vehicles also communicatively coupled with theserver device to a reporting dashboard module.

In another aspect, comparing the telemetry data with the driverobjective data may further comprise an algorithm that may consider anumber of key performance indicators associated with a behavior trait ofthe driver of the vehicle. These performance indicators may comprise alimit data, a route plan data, an engine idling duration data, a maximumrate of acceleration of the vehicle data, and/or a maximum rate ofdeceleration of the vehicle data. According to one aspect, the telemetrydata may comprise of a position of the vehicle, a velocity of thevehicle, a direction of the vehicle, an acceleration of the vehicle, adeceleration of the vehicle, and/or an engine ignition status of thevehicle.

In at least one illustrative aspect, the method may comprise utilizing ageospatial positioning device in a vehicle to receive a telemetry dataassociable with the vehicle on a server device that contains at leastone driver objective data. It may also involve gathering a pattern ofusage information associable with a driver of the vehicle from thetelemetry data and comparing the pattern of usage information associablewith the driver of the vehicle to at least one driver objective datacontained on the server device. A performance score associable with thedriver of the vehicle based on the driver objective data may then begenerated.

According to another aspect, a method of improving a driver's behaviormay comprise utilizing a geospatial positioning device in a vehicle toreceive a telemetry data associable with the vehicle on a server devicethat contains at least one driver objective data. A pattern of usageinformation indicative of a safety rating and/or an efficiency ratingassociable with the driver of the vehicle from the telemetry data maythen be gathered. The method, according to one or more aspects, mayinvolve comparing the pattern of usage information indicative of thesafety rating and/or the efficiency rating and associable with thedriver of the vehicle to at least one driver objective data contained onthe server device and generating a performance score indicative of thesafety rating and/or the efficiency rating associable with the driverand based on the driver objective data.

In another aspect, the performance score indicative of the safety ratingand/or the efficiency rating associable with the driver may be furthercompared to a plurality of performance scores indicative of anothersafety rating and another efficiency rating associable with a pluralityof drivers. The plurality of drivers may then be ranked based on acomparison of the performance scores associable with the plurality ofdrivers. According to one aspect, a competitive situation may thus becreated wherein the outcome of a driver's performance score may dependcritically on the actions of the plurality of drivers that may be a partof the driver's own team and/or fleet. This competitive situation amongdrivers may be created by incorporating components of mathematics,statistics, economics, and psychology to analyze a theory of competitionstated in terms of gains and losses (e.g., the performance score) amongopposing drivers. The goal, according to one or more aspects, would beto improve driver safety and/or efficiency in a non-punitive, yetcompetitive manner.

The methods and systems disclosed herein may be implemented by any meansfor achieving various aspects, and may be executed in a form of amachine-readable medium embodying a set of instructions that, whenexecuted by a machine, cause the machine to perform any of theoperations disclosed herein. Other features will be apparent from theaccompanying drawings and from the detailed description that follows.

BRIEF DESCRIPTION OF THE DRAWINGS

Example embodiments are illustrated by way of example and not limitationin the figures of the accompanying drawings, in which like referencesindicate similar elements and in which:

FIG. 1 illustrates a server device view showing receiving and comparisonof a telemetry data (from a vehicle) with a driver objective data in aserver device, according to one or more embodiments.

FIG. 2 illustrates a module view wherein the methods and systemsdisclosed herein may be implemented by any means for achieving variousaspects, according to one or more embodiments.

FIG. 3 illustrates a table view showing the comparison of a variance toa threshold limit and a generation of a corresponding performance score,according to one or more embodiments.

FIG. 4 is a publishing view illustrating the multiple performance scoresthat may be associable with multiple vehicles, according to one or moreembodiments.

FIG. 5 is a team view that illustrates four teams of multiple vehiclesand corresponding team performance scores and team rankings, accordingto one or more embodiments.

FIG. 6 is a telemetry data view that illustrates various pieces oftelemetry and/or driving pattern data that may comprise the telemetrydata, according to one or more embodiments.

FIG. 7 is a driver objective data view that illustrates various piecesof data that may comprise the driver objective data, according to one ormore embodiments.

FIG. 8 illustrates a server device flow chart view, according to one ormore embodiments.

FIG. 9 illustrates a pattern of usage flow chart view, according to oneor more embodiments.

FIG. 10 illustrates a driver ranking flow chart view, according to oneor more embodiments.

FIG. 11 illustrates a team analytics view, according to one or moreembodiments.

FIG. 12 illustrates a user interface view, according to one or moreembodiments.

FIG. 13 illustrates a team interface view, according to one or moreembodiments.

FIG. 14 is a diagrammatic view of a data processing system in which anyof the embodiments disclosed herein may be performed, according to oneembodiment.

Other features of the present embodiments will be apparent from theaccompanying drawings and from the detailed description that follows.

DETAILED DISCLOSURE

A method of a server device 102 comprising determining that a telemetrydata 106 is associated with a vehicle 104 communicatively coupled withthe server device 102 and comparing the telemetry data 106 with a driverobjective data 108 associated with the vehicle 104 is disclosed.According to one or more embodiments, a variance 302 between thetelemetry data 106 and the driver objective data 108 may be determined.A performance score 306 may be generated upon comparison of the variance302 to the driver objective data 108 and/or a threshold limit 304.According to an illustrative embodiment, the performance score 306 maybe published along with other performance scores of other drivers inother vehicles also communicatively coupled with the server device 102,to a reporting dashboard module 216.

FIG. 1A illustrates a server device view 100, according to one or moreembodiments. Telemetry data 106 from vehicle 104 may be received by aserver device 102 which may have driver objective data 108. The driverobjective data 108 may be resident on the server device 102 and may bepredetermined. The transfer and receiving of the telemetry data 106 fromvehicle 104 by the server device 102 may be based on GPS, RTLS, RFID orOEM telematics. It will be appreciated that the party determining,setting and/or creating the driver objective data 108 may be anorganization. The organization may possess a security interest invehicle 104. The organization may be a corporation, a partnership, anindividual, a government, a non-governmental organization, aninternational organization, an armed force, a charity, a not-for-profitcorporation, a cooperative, or a university. It may be a hybridorganization that may operate in both the public sector and the privatesector, simultaneously fulfilling public duties and developingcommercial market activities, according to one or more embodiments.

According to other embodiments, the party driving vehicle 104 may be anagent of an organization (e.g., a bank, a lender, or any other lendinginstitution or person) that may possess a security interest in vehicle104. The relationship between the driver of vehicle 104 and the partyhaving a security interest in vehicle 104 and/or the party that maypredetermine and/or choose the driver objective data 108, may expresslyor impliedly authorize the party having the security interest and/or thedriver to work under the control and on behalf of the organization. Theparty having the security interest may thus be required to negotiate onbehalf of the organization to secure and/or provide services. Thesecurity interest in vehicle 104 may be a singular security interestassociated with one vehicle or a vehicular loan portfolio securityinterest associated with multiple vehicles, according to one or moreembodiments.

In one or more embodiments, the telemetry data 106 associated withvehicle 104 may be automatically determined based on a situs of vehicle104. The situs may be determined using GPS technology and may be thelocation where vehicle 104 may be treated as being located for legal andjurisdictional purposes, according to one embodiment. The situs may alsobe the place where vehicle 104 is situated (e.g., the impound lot). Itmay also be the temporary and/or permanent location of vehicle 104(e.g., the driver's favorite drinking establishment or the driver'shome). The situs may be a home address or a work address of the driver.The driver may have multiple locations, according to one embodiment.

According to an illustrative example, telemetry data 106 may beassociated with vehicle 104 based on the periodic analysis of thelocation and movement of vehicle 104. The telemetry data 106 may then becompared to the driver objective data 108. This driver objective data108 may include a particular predetermined movement of vehicle 104. Forexample, and according to one or more embodiments, vehicle 104 may havea high rate of acceleration, the driver of vehicle 104 may leave theengine idling for a period of time, vehicle 104 may not have been drivenfor a certain period of time, or vehicle 104 may have been driven, buttoo infrequently (e.g., less than 10 miles). The number of ignitionstarts and stops (e.g., the driver may not have started vehicle 104 fora period of time or may have only started vehicle 104 once in a givenweek) and vehicle 104 decelerating and/or braking suddenly may also becommunicated as telemetry data 106 to be compared with driver objectivedata 108, according to one or more embodiments.

According to another embodiment, the amount of time may vary asdetermined by the party setting, determining and/or choosing the driverobjective data 108, a lender (e.g., a bank or lending institution) or aprovider (e.g., a company selling GPS geospatial positioning devicesand/or a company providing the corresponding web interface to trackvehicles). The party setting, determining and/or choosing the driverobjective data 108 may sell the hardware and/or may provide a softwaresolution to track vehicle 104 and receive telemetry data 106 fromvehicle 104. The predetermined driver objective data 108 and thresholdlimit 304 may be determined by the party having a security interest invehicle 104, according to one or more embodiments.

FIG. 2 illustrates a module view 200 wherein the methods and systemsdisclosed herein may be implemented by any means for achieving variousaspects, according to one or more embodiments. The server module 202 mayperform all tasks associated with the server device 102. The telemetrydata module 204 may collect, categorize, assess and/or analyze telemetrydata 106 associated with vehicle 104. The driver objective data module206 may collect, categorize, assess, select, choose, determine and/oranalyze driver objective data 108 to be compared with telemetry data106. The vehicle module 208 may determine the location of vehicle 104and may associate telemetry data 106 with vehicle 104. The variancemodule 210 may determine the variance 302 between the telemetry data 106and the driver objective data 108 and/or the threshold limit 304,according to one or more embodiments.

The threshold limit module 212 may permit the comparison of the variance302 to a threshold limit 304, according to one embodiment. The thresholdlimit 304 may be the point where the performance score 306 may yieldzero points. According to one or more embodiments, if a driver's ratioof safe deceleration minutes to total driving minutes decreases belowthe threshold limit 304 (e.g., 97%), the driver may receive zero points.If the driver's ratio exceeds the threshold limit 304 (e.g., 97%), thedriver may start scoring points up to a maximum score which may beachieved for a 100% ratio (e.g., a perfect driving record). Theperformance score module 214 may generate a performance score 306 uponcomparison of the variance 302 to a threshold limit 304 and/or thedriver objective data 108. It may also, according to one embodiment,publish the performance score 306 along with other performance scores ofother drivers in other vehicles also communicatively coupled with theserver device 102, to a reporting dashboard module 216. The dashboardmodule 216 may visually indicate and/or publish the performance score306 and other information to be viewed by the driver of vehicle 104 (seeFIGS. 12 and 13). Varying performance scores may be calculated based onthe same driving objectives in a way that may make a fair comparisonbetween drivers with differing driving profiles, according to one ormore embodiments.

The safety and efficiency module 218 may create and implement a driverperformance program in the form of a game and/or a non-punitive, yetchallenging competition among drivers of a plurality of vehicles toincentivize and improve overall driver safety and efficiency. It may,according to one or more embodiments, incorporate components of gametheory that may use one or more mathematical models of devising anoptimum strategy to a given driving situation and/or driving behaviorwherein the driver of vehicle 104 may have the choice of limited andfixed options (e.g., threshold limit 304 and/or driver objective data108). The safety and efficiency module 218 may store and implementalgorithms based on mathematics, statistics, economics, and/orpsychology to improve driver safety and efficiency. It will beappreciated that it may also perform analysis of strategies for dealingwith competitive situations wherein the outcome of a driver's action maydepend critically on the actions of other drivers, according to one ormore embodiments.

FIG. 3 illustrates a table view, according to one or more embodiments.For example, if the threshold limit 304 indicates a value greater thanABCD, the telemetry data 106 and the driver objective data 108 bothregistering exactly ABCD may indicate no variance 302. This may resultin a high performance score 306 (e.g., 94/100). However, if thethreshold limit 304 is less than XYZ and the telemetry data 160registers ZYX and the driver objective data 108 registers XYZ, there maybe a variance 302 and a lower performance score 302 (e.g., 75/100).Similarly, and according to one or more embodiments, if the thresholdlimit 304 for average speed is 70 miles per hour and the driverobjective data 108 indicates a desirable average speed of less than 70miles per hour, the telemetry data 106 indicating that the driver istraveling at an average speed of 75 miles per hour may be indicative ofa variance and a low performance score 306 (e.g., 70/100). According toan illustrative example, if the threshold limit 304 for accelerationrate is 5 miles per second and the driver objective data 108 indicates adesirable acceleration rate of less than 5 miles per second, thetelemetry data 106 indicating that the driver is accelerating at 4 milesper second may not create a variance and thus may lead to a higherperformance score (e.g., 90/100). The performance score 306 associablewith the driver of vehicle 104 may be compared to another performancescore associable with a driver of another vehicle (see FIG. 4),according to one or more embodiments.

According to other embodiments, the telemetry data 106 may comprise, butmay not be limited to, a position of vehicle 104, a velocity of vehicle104, a direction of vehicle 104, an acceleration of vehicle 104, adeceleration of vehicle 104, and/or an engine ignition status of vehicle104 (see FIG. 6). Comparing the telemetry data 106 with the driverobjective data 108 may further comprise an algorithm that may considerseveral key performance indicators associated with a behavior trait ofthe driver of vehicle 104 and may comprise, but may not be limited to, alimit data 702, a route plan data 704, an engine idling duration data706, a maximum rate of acceleration of the vehicle data 708, and/or amaximum rate of deceleration of the vehicle data 710, according to oneor more embodiments (see FIG. 7).

FIG. 4 illustrates a publishing view 400 according to one or moreembodiments. Multiple different vehicles may have associated multipledifferent telemetry data. For example, telemetry data 106A from vehicle104A may be compared to the driver objective data 108 on the serverdevice 102 and may result in a corresponding performance score 306A.Similarly, and according to another embodiment, telemetry data 106B fromvehicle 104B may be compared to the driver objective data 108 on theserver device 102 and may result in a corresponding performance score306B Likewise, telemetry data 106C from vehicle 104C may be compared tothe driver objective data 108 on the server device 102 and may result ina corresponding performance score 306C, according to an illustrativeembodiment. The performance scores 306A, 306B and 306B may be publishedseparately or as a part of a master performance score.

FIG. 5 illustrates a team view 500 according to one or more embodiments.Team A 502 may comprise vehicle 104A1 and vehicle 104A2. Similarly, andaccording to one or more exemplary embodiments, Team B 504 may comprisevehicle 104B1 and vehicle 104B2, Team C 506 may comprise vehicle 104C1and vehicle 104C2, and Team D 508 may comprise vehicle 104D1 and vehicle104D2. Upon comparison of the telemetry data 106 from each vehicle fromeach team, a team ranking 510 may be generated, according to one or moreembodiments. The team ranking 510 may consider the individualperformance of each vehicle in each team as well as the combinedperformance of the vehicles on each team to arrive at a master teamperformance score. According to an illustrative example, a plurality ofdrivers may also be ranked based on a comparison of the performancescores associable with the plurality of drivers.

FIG. 6 illustrates examples of possible telemetry data that may becollected and transmitted to and received by the server device 102 astelemetry data 106 to be compared to the driver objective data 108,according to one or more embodiments. Such telemetry data 106 mayinclude, but is not limited to, position of vehicle 602, velocity ofvehicle 604, direction of vehicle 606, acceleration of vehicle 608, andengine ignition status of vehicle 610. In essence, telemetry data 106may include any and all data that may provide information about vehicle104 (e.g., location, speed, diagnostics etc.) and that may betransmitted to the server device 102, according to one or moreembodiments. Telemetry data 106 may be gathered using a GPS 612 or maybe gathered by taking advantage of the low cost and ubiquity of GlobalSystem for Mobile Communication (GSM) networks by using Short MessagingService (SMS) to receive and transmit telemetry data 106, according toone or more embodiments. According to other embodiments, internationalstandards such as Consultative Committee for Space Data Systems (CCSDS)and/or Inter Range Instrumentation Group (IRIG) may also be implementedto gather and transmit telemetry data 106. According to one or moreexemplary embodiments, portable telemetry, telematics, telecommand, dataacquisition, automatic data processing, Machine to Machine (M2M),Message Queue Telemetry Transport (MQTT), remote monitoring and control,remote sensing, Remote Terminal Unit (RTU), Supervisory Control and DataAcquisition (SCADA), and/or wireless sensor networks may be used and/orimplemented to gather and transfer telemetry data 106 to the serverdevice 102 to be compared with the driver objective data 108 and thethreshold limit 304.

FIG. 7 illustrates examples of possible driver objective data 108 thatmay be resident on the server device 102 and may be compared with thetelemetry data 106, according to one or more embodiments. Such driverobjective data 108 may include, but is not limited to, limit data 702,route plan data 704, engine idling data 706, maximum rate ofacceleration of vehicle data 708, maximum rate of deceleration ofvehicle data 710, maximum average speed data 712, and predetermined usetime data 714. According to one embodiment, the limit data 702 may beassociable with a posted speed limit at a particular geospatial locationsurrounding a present location of vehicle 104 as determined through amapping data source having all posted speed limits in a geospatialvicinity, such that an actual driving behavior data may be compared withthe posted speed limit at the particular geospatial location todetermine whether the variance 302 is beyond the threshold limit 304.According to an illustrative example, if the driver of vehicle 104 isdriving faster than the average speed limit at a given location, hisperformance score 306 would reflect the variance 302 with the thresholdlimit 304 when compared with the desirable driver objective data 108applicable to speed limits.

According to another embodiment, the route plan data may be associablewith a predetermined route plan within the particular geospatiallocation surrounding the present location of the vehicle 104 asdetermined through the mapping data source having all route plans in thegeospatial vicinity, such that the actual driving behavior data iscompared with the route plan at the particular geospatial location todetermine whether the variance 302 is beyond the threshold limit 306.According to an illustrative example, if the driver of vehicle 104varies from a desirable, predetermined and/or given route plan, hisperformance score 306 would reflect the variance 302 with the thresholdlimit 304 when compared with the desirable driver objective data 108applicable to route plans Likewise, an engine idling duration data 706may be used to calculate the amount of time an engine of the vehicle 104is idle in the geospatial vicinity surrounding the present location ofthe vehicle, such that the actual driving behavior data is compared withthe amount of time the engine of the vehicle 104 is idle to determinewhether the variance 302 is beyond the threshold limit 304. According toan illustrative example, if the driver of vehicle 104 varies from adesirable, predetermined and/or given engine idling time, hisperformance score 306 would reflect the variance 302 with the thresholdlimit 304 when compared with the desirable driver objective data 108applicable to engine idling duration.

According to one or more embodiments, a maximum rate of acceleration ofthe vehicle data 708 may be used to measure the rates of acceleration ofthe vehicle 104 in the geospatial vicinity surrounding the presentlocation of the vehicle 104, such that the actual driving behavior datais compared with the maximum rate of acceleration of the vehicle 104 todetermine whether the variance 302 is beyond the threshold limit 304.Similarly, a maximum rate of deceleration of the vehicle data 710 may beused to measure the rates of deceleration of the vehicle 104 in thegeospatial vicinity surrounding the present location of the vehicle 104,such that the actual driving behavior data is compared with the maximumrate of deceleration of the vehicle 104 to determine whether thevariance 302 is beyond the threshold limit 304. According to bothembodiments, if the driver of vehicle 104 varies from a desirable,predetermined and/or given maximum rate of acceleration and/ordeceleration, his performance score 306 would reflect the variance 302with the threshold limit 304 when compared with the desirable driverobjective data 108 applicable to maximum rate of acceleration and/ordeceleration of vehicle 104. According to an illustrative example, thenumber of minutes that the acceleration exceeds the threshold limit 304may also be calculated and compared to the total driving minutes for theperiod. This ratio may be used to compute the driver's performance score306, according to one or more embodiments.

Vehicle 104, according to one or more embodiments, may be a part of afleet of vehicles and may refer to all forms of transportation includingcars, motorcycles, planes, trucks, heavy equipment, jet skis, and allother modes of commercial and/or recreational transportation. The partythat may predetermine the driver objective data 108 and/or may structurea driver performance program (e.g., using game theory) may be a companythat provides GPS devices, GPS vehicle tracking services, OEM telematics(e.g., OnStar™), and/or fleet management services. The company may alsoprovide fleet tracking and mobile asset management services. It may alsobe a sub-prime vehicle finance and/or asset tracking company, afinancial institution, an automobile dealership, a specialty financecompany, a dealership finance company, a bank, a credit union, or aprivate financier in addition to any entity or organization, accordingto one or more exemplary embodiments.

FIG. 8 illustrates a server device flow chart view 800 according to oneor more embodiments. According to FIG. 8 and one or more embodiments, amethod of a server device 102 may comprise determining that a telemetrydata 106 is associated with a vehicle 104 communicatively coupled withthe server device 102 and comparing the telemetry data 106 with a driverobjective data 108 associated with the vehicle 104. According to one ormore embodiments, a variance 302 between the telemetry data 106 and thedriver objective data 108 may be determined. A performance score 306 maybe generated upon comparison of the variance 302 to a threshold limit304. According to an illustrative embodiment, the performance score 306may be published along with other performance scores of other drivers inother vehicles also communicatively coupled with the server device 102to a reporting dashboard module 216.

FIG. 9 illustrates a server device flow chart view 900 according to oneor more embodiments. According to FIG. 8 and one or more embodiments, amethod may comprise utilizing a geospatial positioning device in avehicle 104 to receive a telemetry data 106 associable with the vehicle104 on a server device 102 that contains at least one driver objectivedata 108. The method may involve gathering a pattern of usageinformation associable with a driver of the vehicle 104 from thetelemetry data 108. The pattern of usage information associable with thedriver of the vehicle 104 may be compared to at least one driverobjective data contained 108 on the server device 102 and a performancescore 306 associable with the driver of the vehicle 104 based on thedriver objective data 108 may be generated, according to one or moreexemplary embodiments.

According to an illustrative example, the performance score 306associable with the driver of the vehicle 104 may be compared to anotherperformance score associable with a driver of another vehicle (see FIG.4). The performance score 306 may be based on at least one driverobjective data 108 measured over a predetermined period of time. Amaster score (e.g., a master performance score) may be assigned to ateam of multiple drivers, according to one or more exemplary embodiments(see FIG. 5).

FIG. 10 illustrates a driver ranking flow chart view 1000 according toone or more embodiments. A method of improving a driver's behavior maycomprise utilizing a geospatial positioning device in a vehicle 104 toreceive a telemetry data 106 associable with the vehicle 104 on a serverdevice 102 that may contain at least one driver objective data 108. Apattern of usage information indicative of a safety rating and/or anefficiency rating associable with the driver of the vehicle 104 may begathered from the telemetry data 106. Thereafter, the pattern of usageinformation indicative of the safety rating and/or the efficiency ratingand associable with the driver of the vehicle 104 may be compared to atleast one driver objective data 108 contained on the server device 102.

According to one or more exemplary embodiments, a performance score 306indicative of the safety rating and/or the efficiency rating associablewith the driver and based on the driver objective data 108 may begenerated. It will be appreciated that, according to one embodiment, theperformance score 306 indicative of the safety rating and/or theefficiency rating associable with the driver may be further compared toa plurality of performance scores indicative of another safety ratingand another efficiency rating associable with a plurality of drivers(see FIGS. 4 and 5). The method, according to one embodiment, mayinvolve ranking the plurality of drivers based on a comparison of theperformance scores associable with the plurality of drivers.

FIG. 11 illustrates a team analytics view 1100, according to one or moreembodiments. A team driving challenge and/or a driver performanceprogram may be created and implemented to improve driver safety and/orefficiency according to one or more embodiments. Various metrics may beused and implemented to this extent (e.g., as predetermined driverobjective data 108), including but not limited to, percentage of minutesdriving at and/or below the posted speed limit, percentage of drivingminutes without a hard braking incident, percentage of authorizeddriving to total driving, percentage of driving minutes without anacceleration incident, percentage of minutes moving when engine isrunning, percentage of worked days with on-time daily disposition, etc.Each above mentioned metric may be assigned a scoring factor that mayinclude a brake score, a speed score, an acceleration score, an idlingscore, etc. All these scores, according to one or more embodiments, maybe combined to give rise to a total score. Teams of a plurality ofdrivers would then be ranked accordingly and a scaled score may beassigned to different teams based on the performance of individualdrivers within that team (e.g., see Team A 502, Team B 504, Team C 506and Team D 508 of FIGS. 5 and 11). FIGS. 12 and 13 illustrate a userinterface view 1200 and a team interface view 1300 respectively,according to one or more embodiments.

According to an illustrative example, a method for improving commercialdriver safety and efficiency may involve using individual and teamcompetition based on actual driver behavior. According to oneembodiment, the method may be used for improving the safety andefficiency of drivers in commercial vehicle fleets. Each driver may havea GPS tracking module installed in his/her vehicle. The GPS module maytransmit vehicle telemetry (e.g., telemetry data 106) back to a centralserver (e.g., server device 102). According to one or more embodiments,vehicle telemetry may include (but may not be limited to), position,velocity, direction, acceleration, and/or engine on/off status ofvehicle 104. The server device 102 may contain information on driverobjectives (e.g., driver objective data 108). These objectives,according to one or more embodiments, may include (but may not belimited to), posted speed limits, route plans, engine idling durations,maximum rate of vehicle acceleration and/or deceleration, days/hours forapproved vehicle use etc.

According to one or more exemplary embodiments, each driver may have anaverage ratio of minutes spent driving at or below the posted speedlimit (e.g., limit date 702 of FIG. 7) to the total number of minutesspent driving. The ratio for each objective may be converted tonumerical scores (e.g., see FIGS. 12 and 13), for each driver. Eachdriver may then be given an aggregate score resulting from a combinationof individual objective scores, according to one embodiment. Accordingto another embodiment, each driver may be assigned to a team of drivers(e.g., see FIG. 5). Each team may have a score that may be a combinationof individual driver scores. According to one or more embodiments, eachteam may participate in a multi-week scoring competitions. Winning teamsmay be calculated at intervals throughout the competition season. Thefinal interval of seasons may be a championship competition betweenseason leading teams. A new season, according to one embodiment, maybegin after completion of the final interval, with all scores reset tozero.

According to an illustrative example, a 12 week season may runsequentially throughout the year. The teams may be ranked at the end ofeach week, and winners may be calculated. Week 12, according to oneembodiment, may be the “Superbowl of Driving Week.” Top teams from the“regular” season may be eligible to compete in the final week ofcompetition for the grand champion award. According to otherembodiments, all individual and team scores would be reset to zero, anda new competition reason would begin.

It will be appreciated that, according to one or more embodiments,central servers (e.g., server device 102) may share live and historicalscoring information to drivers in a variety of matters including but notlimited to, web-based applications, mobile applications (e.g., see FIGS.12 and 13), periodic emails, and/or periodic SMS messages. This mayallow all drivers to access current scoring and ranking information forall teams and individuals in the competition. In one or more exemplaryembodiments, commercial fleet managers may have the opinion ofestablishing an incentive plan based on driver and/or team performancein the competition. It will be appreciated that, the combination ofinherent driver competitiveness and optional incentive programs maycause drivers to improve their driving performance with respect to theobjectives (e.g., driver objective data 108) established by the fleetmanager, according to one or more embodiments.

According to other embodiments, driver behavior may be positivelyimpacted by providing trend information directly to the driver in aconstructive fashion. This method may eliminate management in the“review mirror.” It will be appreciated that, according to one or moreexemplary embodiments, the driver performance program may work as acontest and/or a game with drivers competing as teams as well as forindividual incentives. Drivers, according to one embodiment, may have aview into and/or access to summary and/or trend information of theiroverall performance (e.g., see FIGS. 12 and 13). Drivers may be able todrill down into the specific aspects of their driving behavior and/orperformance such as speeding, idle time, and/or aggressive driving(e.g., a fast rate of acceleration and/or a hard braking incident).

According to one or more illustrative embodiments, direct summaryfeedback to the driver in a game and/or contest format may incentivize,coach and/or influence the driver to improve his/her driving safety andefficiency. The driver safety program may have a mobile applicationdashboard (e.g., see FIGS. 12 and 13). It may affect change at thedriver level by implementing game and/or contest aspects such as teamcompetition, individual recognitions (e.g., most valuable driver, poleposition winner, race winner, etc.), configurable seasons (e.g., dates,duration, etc.), the ability to see the performance of other teams andteammates, and/or collaboration and/or communication between variousteam members.

According to other exemplary embodiments, driver performance may bescored and/or monitored in the following areas, including but notlimited to, engine idling time, speeding, hard braking incidents, andhard acceleration incidents, etc. According to one embodiment, onlytrending data may be displayed in the dashboard module 216 (not specificincident data). The initial user-interface screen may indicate driverperformance as well as relative performance (compared to other teams andother drivers) (e.g., see FIGS. 12 and 13). Such data and informationmay be visible in near real-time to all drivers. The goal of the methodmay be to tie merits and incentives and to create a relationship withthe driver, according to one or more embodiments. It will also beappreciated that, such performance data and information may be shared onsocial media websites such as Facebook®, Twitter®, etc., according toone or more embodiments.

Although the present embodiments have been described with reference tospecific example embodiments, it will be evident that variousmodifications and changes may be made to these embodiments withoutdeparting from the broader spirit and scope of the various embodiments.For example, the various devices (e.g., the server device 102), modules,analyzers, generators, etc. described herein may be enabled and operatedusing hardware circuitry (e.g., CMOS based logic circuitry), firmware,software and/or any combination of hardware, firmware, and/or software(e.g., embodied in a machine readable medium). For example, the variouselectrical structure and methods may be embodied using transistors,logic gates, and electrical circuits (e.g., application specificintegrated (ASIC) circuitry and/or in Digital Signal Processor (DSP)circuitry). For example, data transmission technologies, geospatialpositioning devices, and devices other than ones employing GPStechnology (e.g., RFID, RTLS, OEM telematics, location detection basedon cell phone towers, electromagnetic waves, optical emissions,infrared, radar, sonar, radio, Bluetooth™ etc.) may be used to transmittelemetry data 106 for the purposes of the invention described herein,according to one or more exemplary embodiments.

Particularly, several modules as illustrated in FIG. 2 may be employedto execute the present embodiments. The telemetry data module 204, theserver module 202, the driver objective data module 206, the vehiclemodule 208, the variance module 210, the threshold module 212, theperformance score module 214, the dashboard module 216, the safety &efficiency module 218, and all other modules of FIGS. 1-14 may beenabled using software and/or using transistors, logic gates, andelectrical circuits (e.g., application specific integrated ASICcircuitry) such as a security circuit, a recognition circuit, a dynamiclandmark circuit, an ignition event circuit, a store circuit, atransform circuit, an ICE circuit, and other circuits.

FIG. 14 may indicate a personal computer and/or the data processingsystem in which one or more operations disclosed herein may beperformed. The processor 1402 may be a microprocessor, a state machine,an application specific integrated circuit, a field programmable gatearray, etc. (e.g., Intel® Pentium® processor, 620 MHz ARM1176®, etc.).The main memory 1404 may be a dynamic random access memory, anon-transitory memory, and/or a primary memory of a computer system. Thestatic memory 1406 may be a hard drive, a flash drive, and/or othermemory information associated with the data processing system. The bus1408 may be an interconnection between various circuits and/orstructures of the data processing system. The video display 1410 mayprovide graphical representation of information on the data processingsystem. The alpha-numeric input device 1412 may be a keypad, a keyboard,a virtual keypad of a touchscreen and/or any other input device of text(e.g., a special device to aid the physically handicapped). The cursorcontrol device 1414 may be a pointing device such as a mouse. The driveunit 1416 may be the hard drive, a storage system, and/or other longerterm storage subsystem. The signal generation device 1418 may be a biosand/or a functional operating system of the data processing system. Thenetwork interface device 1420 may be a device that performs interfacefunctions such as code conversion, protocol conversion and/or bufferingrequired for communication to and from the network 1426. The machinereadable medium 1428 may provide instructions on which any of themethods disclosed herein may be performed. The instructions 1424 mayprovide source code and/or data code to the processor 1402 to enable anyone or more operations disclosed herein.

In addition, it will be appreciated that the various operations,processes, and methods disclosed herein may be embodied in amachine-readable medium and/or a machine accessible medium compatiblewith a data processing system (e.g., a computer system), and may beperformed in any order (e.g., including using means for achieving thevarious operations). Accordingly, the specification and drawings are tobe regarded in an illustrative rather than a restrictive sense.

What is claimed is:
 1. A method of a server device, comprising:determining, through the server device, telemetry data is associatedwith a vehicle of a plurality of vehicles communicatively coupled incontact with the server device; comparing, through a processor of theserver device communicatively coupled to a memory, the telemetry datawith driver objective data associated with a driver of the vehicle;determining, through the processor, a total variance between thetelemetry data and the driver objective data, wherein determining thetotal variance between the telemetry data and the driver objective datafurther comprises executing an algorithm by the processor, wherein thealgorithm, comprises: calculating differences between the vehicle'stelemetry data to speed limit data, engine idling duration data, vehicleacceleration data, and vehicle deceleration data of the driver objectivedata, dividing the calculated differences by their respective driverobjective data to obtain percentage values, and adding the percentagevalues to obtain the total variance; generating, through the processor,a performance score of the driver indicative of an efficiency ratingassociable with the driver, based on the total variance; and displaying,through a dashboard display of the vehicle through a reporting module,the performance score of the driver, along with performance scores ofother drivers driving the plurality of vehicles, wherein: the speedlimit data is a posted speed limit at a particular geospatial locationsurrounding a present location of the vehicle as determined through amapping data source of the server device and calculating the differencebetween the vehicle's telemetry data to the speed limit data comprisescalculating the difference between the vehicle's average speed and theposted speed limit, the engine idling duration data is a predeterminedand preferred amount of time an engine of a vehicle is idle in thegeospatial vicinity surrounding the present location of the vehicle andcalculating the difference between the vehicle's telemetry data to theengine idling duration data comprises calculating the difference betweenthe vehicle's engine idling duration time and the predetermined andpreferred amount of time, the vehicle acceleration data is apredetermined and preferred average acceleration rate of a vehicle inthe geospatial vicinity surrounding the present location of the vehicleand calculating the difference between the vehicle's telemetry data tothe vehicle acceleration data comprises calculating the differencebetween the vehicle's average acceleration rate and the predeterminedand preferred average acceleration rate, and the vehicle decelerationdata is a predetermined and preferred average deceleration rate of avehicle in the geospatial vicinity surrounding the present location ofthe vehicle and calculating the difference between the vehicle'stelemetry data to the vehicle deceleration data comprises calculatingthe difference between the vehicle's average deceleration rate and thepredetermined and preferred average deceleration rate.
 2. The method ofclaim 1, further comprising comparing the performance score associatedwith the driver to the plurality of performance scores associated withthe other drivers.